Practical Time Series Forecasting with Python: A Hands-On Guide
Practical Time Series Forecasting with Python: A Hands-On Guide provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the free open-source Python software to develop effective forecasting solutions that extract business value from time series data. This edition includes:
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- Popular forecasting methods including smoothing algorithms, regression models, ARIMA, neural networks, deep learning, and ensembles
- A practical approach to evaluating the performance of forecasting solutions
- A business-analytics exposition focused on linking time-series forecasting to business goals
- Guided cases for integrating the acquired knowledge using real data
- End-of-chapter problems to facilitate active learning
- Data, Python code, and instructor materials on companion website
- Affordable and globally-available textbook, available in hardcover, paperback, and ebook formats
Practical Time Series Forecasting with Python: A Hands-On Guide
Practical Time Series Forecasting with Python: A Hands-On Guide provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the free open-source Python software to develop effective forecasting solutions that extract business value from time series data. This edition includes:
- Popular forecasting methods including smoothing algorithms, regression models, ARIMA, neural networks, deep learning, and ensembles
- A practical approach to evaluating the performance of forecasting solutions
- A business-analytics exposition focused on linking time-series forecasting to business goals
- Guided cases for integrating the acquired knowledge using real data
- End-of-chapter problems to facilitate active learning
- Data, Python code, and instructor materials on companion website
- Affordable and globally-available textbook, available in hardcover, paperback, and ebook formats
35.0
In Stock
5
1

Practical Time Series Forecasting with Python: A Hands-On Guide
256
Practical Time Series Forecasting with Python: A Hands-On Guide
256Paperback
$35.00
35.0
In Stock
Product Details
ISBN-13: | 9780997847963 |
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Publisher: | Axelrod Schnall Publishers |
Publication date: | 07/05/2025 |
Pages: | 256 |
Product dimensions: | 7.00(w) x 10.00(h) x 0.54(d) |
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